Evidence map›Paper›PMID 40894534›Full record

ArticlebioRxiv : the preprint server for biology2025

Correctness is its own reward: bootstrapping error signals in self-guided reinforcement learning.

Ziyi Gong, Fabiola Duarte, Richard Mooney, John Pearson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ziyi GongDepartment of Neurobiology, Duke University, Durham, NC, USA.ORCID 0000-0002-0453-7468
Fabiola DuarteDepartment of Neurobiology, Duke University, Durham, NC, USA.ORCID 0000-0001-6147-7978
Richard MooneyDepartment of Neurobiology, Duke University, Durham, NC, USA.ORCID 0000-0002-3308-1367
John PearsonDepartment of Neurobiology, Duke University, Durham, NC, USA.ORCID 0000-0002-9876-7837

Funding

Mechanisms for internally and externally guided sensorimotor learningR01NS099288 · NINDS · DUKE UNIVERSITY · PI MOONEY, RICHARD D · 2016 to 2025
$3.9M
Corticostriatal contributions to motor exploration and reinforcementR01NS118424 · NINDS · DUKE UNIVERSITY · PI GARDNER, TIMOTHY JAMES, LOIS, CARLOS · 2023 to 2024
$2.3M
Real-time mapping and adaptive testing for neural population hypothesesRF1DA056376 · NIDA · DUKE UNIVERSITY · PI PEARSON, JOHN · 2022 to 2023
$1.4M
NIDA NIH HHS RF1 DA056376NINDS NIH HHS R01 NS099288NINDS NIH HHS R01 NS118424
6 · The paper itself

Abstract

Reinforcement learning (RL) offers a compelling account of how agents learn complex behaviors by trial and error, yet RL is predicated on the existence of a reward function provided by the agent's environment. By contrast, many skills are learned without external guidance, posing a challenge to RL's ability to account for self-directed learning. For instance, juvenile male zebra finches first memorize and then train themselves to reproduce the song of an adult male tutor through extensive practice. This process is believed to be guided by an internally computed assessment of performance quality, though the mechanism and development of this signal remain unknown. Here, we propose that, contrary to prevailing assumptions, tutor song memorization and performance assessment are subserved by the same neural circuit, one trained to predictively cancel tutor song. To test this hypothesis, we built models of a local forebrain circuit that learns to use contextual input from premotor regions to cancel tutor song auditory input via plasticity at different synaptic loci. We found that, after learning, excitatory projection neurons in these circuits exhibited population error codes signaling mismatches between the tutor song memory and birds' own performance, and these signals best matched experimental data when networks were trained with anti-Hebbian plasticity in the recurrent pathway through inhibitory interneurons. We also found that model learning proceeds in two stages, with an initial phase of sharpening error sensitivity followed by a fine-tuning period in which error responses to the tutor song are minimized. Finally, we showed that the error signal produced by this model can train a simple RL agent to replicate the spectrograms of adult bird songs. Together, our results suggest that purely local learning via predictive cancellation suffices for bootstrapping error signals capable of guiding self-directed learning of natural behaviors.

Identifiers

PMID40894534
PMCPMC12393258

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.